MATLAB: How to load own data set into neural network

classificationdatasetDeep Learning Toolboxiris_dataneural networkpatternnet

Hi,
I am fairly new to MATLAB and I would like help in understanding about datasets. For classification in neural network, the example for wine classification show:
[x,t] = wine_dataset;
size(x)
size(t)
net = patternnet(10);
view(net)
I have a dataset of input [8×4]matrix and target [4×4]matrix. How do I input this into neural network such that I can use the function patternnet?
Thanks in advance for your help.

Best Answer

Exactly as indicated in
help patternnet
and
doc patternnet
where
x = yourinput;
t = yourtarget; % target columns should be unit vectors with a single 1
DOCUMENTATION BUG: the default input is (10,'trainscg') NOT (20,'trainlm')!
close all, clear all, clc
 [ x, t ] = iris_dataset;
 [ I N ]  = size(x)         % [ 4 150 ]
 [ O N ]  = size(t)         % [ 3 150 ]
 trueclass  = vec2ind(t);
 MSE00  = mean(var(t',1)) % 0.222 biased MSE for naive constant output model
 MSE00a = mean(var(t',0)) % 0.224 unbiased MSE for naive constant output model
 Ntrn    = N - 2*(0.15*N) % 105 default size of training set
 Ntrneq  = Ntrn*O         % 315 Number of training equations
% Nw = (I+1)*H+(H+1)*O % No. of unknown weights for H hidden nodes
 Hub = -1 + ceil( (Ntrneq-O)/(I+O+1))% 38 Upperbound for H if Ntrneq > Nw
 H   = floor(Hub/10)% 3 Desire Ntrneq >> Nw to mitigate noise and errors in data
 Nw   = (I+1)*H+(H+1)*O %  27 Number of unknown weights
 Ndof = Ntrneq - Nw % 288 No. of estimation degrees of freedom
(See Wikipedia reference below)
 MSEgoal = 0.01*Ndof*MSE00a/Ntrneq % 0.002 Desire adjusted R^2 >= 0.99
http://en.wikipedia.org/wiki/Adjusted_R-squared
 MinGrad = MSEgoal/10             %  2.05e-4
 rng(0)                           % Initialize RNG
 net = patternnet(H);
 net.trainParam.goal     = MSEgoal;
 net.trainParam.min_grad = MinGrad;
 [net tr Y E ] = train(net,x,t);   % E = t-Y
 stopcrit = tr.stop             % Validation stop.
 numepochs = tr.num_epochs      % 60
 bestepoch = tr.best_epoch      % 54
 MSE = tr.perf(bestepoch+1)     % 0.0115
 MSEa = Ntrneq*MSE/Ndof         % 0.0126
 R2 = 1-MSE/MSE00               % 0.948
 R2a = 1-MSEa/MSE00a            % 0.944
 assignedclass = vec2ind(Y);
 err = assignedclass~=trueclass;
 Nerr = sum(err)                % 2
 PctErr = 100*Nerr/N            % 1.33
result = [ H numepochs bestepoch R2 R2a Nerr PctErr]
result = [ 3 60 54 0.948 0.944 2 1.33 ]
% NOTE1: Can use tr to obtain trn/val/tst breakdown
%NOTE2: To imprcve performance try other (e.g.,9 more) random weight initializations. If that doesn't work try 10 random weight initialization trials with H = 4.
Hope this helps.
Thank you for formally accepting my answer
Greg
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